Graduation Year


Document Type




Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Grisselle Centeno


Data mining, Simulation, Stochastic procedure duration, Support Vector Regression, Surgical Data


An operating room (OR) is considered to be one of the most costly functional areas within hospitals as well as its major profit center. It is known that managing an OR department is a challenging task, which requires the integration of many actors (e.g., patients, surgeons, nurses, technicians) who may have conflicting interests and priorities.

Considering these aspects, this dissertation focuses on developing a simulation based methodology for scheduling operating rooms under uncertainty, which reflects the complexity, uncertainty and variability associated with surgery.

We split the process of scheduling ORs under uncertainty into two main components. First, we designed a research roadmap for modeling surgical procedure duration (from incision to wound closure) based on the surgery volume and time variability. Then, using a real surgical dataset we modeled the procedure duration using parametric and distribution-free predictive methods. We found that Support Vector Regression performs better that Generalized Linear Models increasing the prediction accuracy on unseen data by at least 5.5%.

Next, we developed a simulation based methodology for scheduling ORs through a case study. For that purpose, we initially built one day feasible schedules using the 60th, 70th, 80th, and 90th percentiles to allocate surgical procedures to ORs using four different allocation policies. We then used a discrete event simulation model to evaluate the robustness of these initial feasible schedules considering the stochastic duration of all the OR activities and the arrival of surgical emergency cases. We found that on average elective waiting almost doubled the time for the emergency cases. In addition, we observed that there is not a clear effect of how being more conservative in scheduling within each scheduling policy impacts the elective waiting times. By contrast, there is a clear effect of how the scheduling policy and scheduling percentile impact the emergency waiting times. Thus, as we increase the percentile, the waiting times for emergency cases remarkably increases under half of the scheduling policies but reflects a lesser impact under scheduling the other half. OR utilization and OR overtime in a "virtual" eight operating room hospital fluctuate between 67% and 88% and 97 and 111 minutes respectively. Moreover, we noticed that both performance metrics depend not only on the levels of the scheduling policy and scheduling percentile but also are strongly affected by the increase of the emergency arrival rate.

Finally, we fit a multivariate-multiple-regression model using the output of the simulation model to assess the robustness of the model and the extent to which these results can be generalized to a single, aggregate hospital goal. Further research should include a true stochastic optimization model to integrate optimization techniques into simulation analysis.